In cities and other localities, there is an increasingly large number of surveillance cameras used in the streets, schools, hospitals, stadiums, and other public places. A large quantity of surveillance video is produced every day, which puts a great deal of pressure on facilities for storing the surveillance video and those who must study the surveillance video, e.g., law enforcement officials.
Conventionally, various processes may be used to generate a video summary from surveillance video. In the generation of a video summary, e.g., a video abstract, based on motive objects, extracting motion foreground objects may be done by first detecting the motion area and cutting the motive object out of the source frame. Then, the motive object picture is integrated into the corresponding background image by erasing the corresponding location on the background picture and putting the motive object image in its place. Because of the effects of light changes, foreground images tend not to blend well into the background, and they leave a clear border shadow, which negatively affects the quality of the generated summary video.
In surveillance video, the most influential factor in the background is light. To adapt a background to the actual environment, an algorithm may be used to update the background. However, due to the large amount of video data in typical installations, and the complexity of the applied algorithms, background updating requires a significant amount of time, which significantly affects the speed of video summary generation.